Lithology and fluid prediction from seismic attributes using supervised Bayesian classification

@inproceedings{CrozatRM2007,
 abstract = { Traditional reservoir modeling combines data from various origins, which give different and complementary information. Well data give precise geological information about a very small part of the subsurface. On the other hand seismic data give information in 3D, but this information is at a lower vertical resolution and can not directly be linked to rock properties. If we want to combine these two sources of information to model a reservoir, we need to convert the seismic data into something more meaningful from a geological point of view. This paper presents a method and its implementation in the Gocad platform that can be used to make this conversion. },
 author = { Crozat, Thierry },
 booktitle = { 27th gOcad Meeting },
 month = { "may" },
 publisher = { ASGA },
 title = { Lithology and fluid prediction from seismic attributes using supervised Bayesian classification },
 year = { 2007 }
}